Evidence map›Paper›PMID 42223818›Full record

ArticleRadiological physics and technology2026

Evaluation of super-resolution deep learning reconstruction on three-dimensional constructive interference in steady state for enhanced visualization of vestibular schwannomas.

Akira Katayama, Koichiro Yasaka, Miori Fujimoto, Yusuke Asari, Satoru Kamio, Jun Kanzawa, Yuki Sonoda, Shigeru Kiryu, Osamu Abe

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiological physics and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Akira KatayamaDepartment of Radiology, The University of Tokyo, Tokyo, Japan.ORCID http://orcid.org/0000-0001-9677-1421
Koichiro YasakaDepartment of Radiology, The University of Tokyo, Tokyo, Japan. koyasaka@gmail.com.ORCID http://orcid.org/0000-0002-0324-6562
Miori FujimotoDepartment of Radiology, The University of Tokyo, Tokyo, Japan.
Yusuke AsariDepartment of Radiology, The University of Tokyo, Tokyo, Japan.
Satoru KamioDepartment of Radiology, The University of Tokyo, Tokyo, Japan.
Jun KanzawaDepartment of Radiology, The University of Tokyo, Tokyo, Japan.
Yuki SonodaDepartment of Radiology, The University of Tokyo, Tokyo, Japan.
Shigeru KiryuDepartment of Radiology, International University of Health and Welfare, Chiba, Japan.
Osamu AbeDepartment of Radiology, The University of Tokyo, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We evaluated whether, compared with conventional deep learning reconstruction (DLR) and zero-filling interpolation (ZIP), super-resolution DLR (SR-DLR) enhances the visualization of vestibular schwannomas and cranial nerves in three-dimensional constructive interference in steady state (3D-CISS) magnetic resonance (MR) imaging. This retrospective study analyzed 39 patients with vestibular schwannomas who underwent 3T MR imaging with 3D-CISS. Axial images were reconstructed using SR-DLR, DLR, and ZIP. Three readers independently evaluated tumor depiction, depiction of facial and vestibulocochlear nerves, sharpness, noise, artifacts, and diagnostic acceptability. Quantitative indicators included edge rise distance (ERD) and edge rise slope (ERS) measured along a linear region of interest (ROI) drawn perpendicularly across the tumor-cistern cerebrospinal fluid (CSF) boundary, and signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and contrast ratio (CR) calculated from circular ROIs (3-5 mm) placed in the tumor and prepontine CSF. SR-DLR significantly improved visualization of vestibular schwannoma and facial and vestibulocochlear nerves compared with ZIP (p < 0.001). Nerve depiction ratings varied between readers when comparing SR-DLR and DLR. SR-DLR yielded significantly better sharpness, lower ERD and higher ERS than both DLR and ZIP (p < 0.001). Artifacts showed no significant differences. No clear deterioration in noise was noted. Objectively, SR-DLR showed significantly higher CR than DLR (p = 0.023) and ZIP (p < 0.001). SNR and CNR were significantly higher with SR-DLR than ZIP, but comparable to DLR. SR-DLR improves sharpness and lesion conspicuity on 3D-CISS imaging without a clear increase in artifacts or noise compared with ZIP.

Indexed as

Deep LearningImaging, Three-DimensionalMagnetic Resonance ImagingNeuroma, AcousticAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesSignal-To-Noise RatioYoung AdultCerebellopontine angleMagnetic resonanceMR cisternographySuper-resolution deep learning reconstructionVestibular schwannomaVestibulocochlear nerve

Identifiers

PMID42223818
PMCPMC13546317

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.